Imperial College London
Portrait Picture

Professor Cleo Kontoravdi

Faculty of Engineering, Department of Chemical Engineering

Professor of Biological Systems Engineering
 
 
 
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Contact

 

+44 (0)20 7594 6655cleo.kontoravdi98 Website

 
 
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Location

 

310ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Antonakoudis:2020:10.1016/j.csbj.2020.10.011,
author = {Antonakoudis, A and Barbosa, R and Kotidis, P and Kontoravdi, K},
doi = {10.1016/j.csbj.2020.10.011},
journal = {Computational and Structural Biotechnology Journal},
pages = {3287--3300},
title = {The era of big data: Genome-scale modelling meets machine learning},
url = {http://dx.doi.org/10.1016/j.csbj.2020.10.011},
volume = {18},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.
AU  - Antonakoudis,A
AU  - Barbosa,R
AU  - Kotidis,P
AU  - Kontoravdi,K
DO  - 10.1016/j.csbj.2020.10.011
EP  - 3300
PY  - 2020///
SN  - 2001-0370
SP  - 3287
TI  - The era of big data: Genome-scale modelling meets machine learning
T2  - Computational and Structural Biotechnology Journal
UR  - http://dx.doi.org/10.1016/j.csbj.2020.10.011
UR  - http://hdl.handle.net/10044/1/84715
VL  - 18
ER  -
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